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国土资源遥感  2017, Vol. 29 Issue (3): 41-50    DOI: 10.6046/gtzyyg.2017.03.06
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于可变尺度Mean-Shift的农田高分遥感影像分割算法
苏腾飞, 张圣微, 李洪玉
内蒙古农业大学水利与土木建筑工程学院,呼和浩特 010018
Variable scale Mean-Shift based method for cropland segmentation from high spatial resolution remote sensing images
SU Tengfei, ZHANG Shengwei, LI Hongyu
College of Water Conservancy and Civil Engineering, Inner Mongolian Agricultural University, Hohhot 010018, China
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摘要 为了提升农田高分遥感影像(high spatial resolution remote sensing image,HRI)的信息提取效果,提出了一种新的农田HRI分割算法。传统的Mean-Shift(MS)HRI分割算法仅利用全局或单一的尺度参数; 而常规可变尺度MS算法在尺度参数估算中也只考虑光谱信息。这些都导致其分割结果难以完整地展现不同尺度的农田区域。针对该问题,在MS算法的基础上进行了改进: 第一,提出了一种局部可变尺度参数的估计方法; 第二,提出了利用局部可变尺度进行MS滤波的模型函数。该改进算法主要包含3步: ①为了全面考虑不同波段的响应变化,在MS滤波核函数中采用了对角化的尺度参数矩阵,并将其与采样点密度估计模型相结合,导出了一种可变尺度MS滤波的迭代函数; ②为了提高算法的自动化程度,利用局部光谱变化与边界强度信息,提出了一种新的局部尺度参数估算方法; ③将MS滤波结果输入到基于分形网络演化方法(fractal net evolution approach,FNEA)的空间聚类算法中,得到最终的分割结果。利用RapidEye与OrbView3的2景HRI进行了算法验证。实验结果表明,所提出的改进算法能够优化农田HRI分割的精度。
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关键词 景观生态敏感性GISKriging插值榆林    
Abstract:In order to improve the effect of information extraction from high spatial resolution remote sensing images (HRI) of cropland, the authors put forward a new HRI segmentation algorithm. Due to the fact that the traditional Mean-Shift (MS) segmentation method only uses a global and single scale, and that some variable bandwidth MS only considers spectral information in their scale estimation process, and croplands with various sizes could be hardly extracted in one segmentation result, the authors improved a MS based approach to tackle this problem. The main consideration lies in two aspects: ① A local variable scale parameter estimation method is proposed; ② The model function for local variable scale is established for MS filtering. The proposed approach mainly consists of 3 parts: ① With the objective of comprehensively considering the response variation of different bands, the diagonal scale parameter matrix is adopted in the kernel function of MS filtering, and it is combined with sample point estimation model to derive the iterative function for variable scale MS filtering; ② For the purpose of increasing automation of the proposed method, local spectral variation and edge strength information are utilized to design a new local scale parameter estimation method; ③ For obtaining the final segmentation, the filtering result is used as input for the fractal net evolution approach (FNEA) which is a spatial clustering method. Two scenes of HRI acquired by RapidEye and OrbView3 were employed for experiment, and the results show that the proposed method can optimize the accuracy of cropland HRI segmentation.
Key wordslandscape ecological sensitivity    GIS    Kriging interpolation    Yulin
收稿日期: 2015-08-17      出版日期: 2017-08-15
基金资助:国家自然科学基金项目“科尔沁沙地典型生态系统水热通量传输机理及其与植被耦合关系试验和模拟研究”(编号: 51569017)、“内蒙古典型草原水文过程及其扰动与触发草地退化的水文临界条件实验与模拟研究”(编号: 51269014)、内蒙古自然科学基金项目“半干旱区沙地典型生态系统水热通量传输机理研究”(编号: 2015MS0514)和中国博士后科学基金面上资助项目“西部地区博士后人才资助计划”(编号: 2015M572630XB)共同资助
通讯作者: 张圣微(1979-),男,博士,教授,主要从事定量遥感、生态水文及气候变化等方面的研究。Email:zsw_imau@163.com
作者简介: 苏腾飞(1987-),男,硕士,实验师,主要从事面向对象的遥感图像分析算法方面的研究。Email:stf1987@126.com。
引用本文:   
苏腾飞, 张圣微, 李洪玉. 基于可变尺度Mean-Shift的农田高分遥感影像分割算法[J]. 国土资源遥感, 2017, 29(3): 41-50.
SU Tengfei, ZHANG Shengwei, LI Hongyu. Variable scale Mean-Shift based method for cropland segmentation from high spatial resolution remote sensing images. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 41-50.
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https://www.gtzyyg.com/CN/10.6046/gtzyyg.2017.03.06      或      https://www.gtzyyg.com/CN/Y2017/V29/I3/41
[1] Blaschke T,Hay G J,Kelly M,et al.Geographic object-based image analysis-towards a new paradigm[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87:180-191.
[2] Kim H O,Yeom J M.Effect of red-edge and texture features for object-based paddy rice crop classification using Rapideye multi-spectral satellite image data[J].International Journal of Remote Sensing,2014,35(19):7046-7068.
[3] Vieira A,Formaggio A R,Rennó C D,et al.Object based image analysis and data mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas[J].Remote Sensing of Environment,2012,123:553-562.
[4] Peña-Barragán J M,Ngugi M K,Plant R E,et al.Object-based crop identification using multiple vegetation indices,textural features and crop phenology[J].Remote Sensing of Environment,2011,115(6):1301-1316.
[5] Liu M W,Ozdogan M,Zhu X J.Crop type classification by simultaneous use of satellite images of different resolutions[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(6):3637-3649.
[6] Ming D P,Li J,Wang J Y,et al.Scale parameter selection by spatial statistics for GEOBIA:Using mean-shift based multi-scale segmentation as an example[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,106:28-41.
[7] Lang F K,Yang J,Li D R,et al.Mean-shift-based speckle filtering of polarimetric SAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(7):4440-4454.
[8] Michel J,Youssefi D,Grizonnet M.Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(2):952-964.
[9] Comaniciu D,Meer P.Mean shift:A robust approach toward feature space analysis[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603-619.
[10] Wang L G,Liu G Y,Dai Q L.Optimization of segmentation algorithms through mean-shift filtering preprocessing[J].IEEE Geoscience and Remote Sensing Letters,2014,11(3):622-626.
[11] Ponti M P.Segmentation of low-cost remote sensing images combining vegetation indices and mean shift[J].IEEE Geoscience and Remote Sensing Letters,2013,10(1):67-70.
[12] Banerjee B,Varma S,Buddhiraju K M,et al.Unsupervised multi-spectral satellite image segmentation combining modified mean-shift and a new minimum spanning tree based clustering technique[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2014,7(3):888-894.
[13] Huang X,Zhang L P.An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(12):4173-4185.
[14] Comaniciu D,Ramesh V,Meer P.The variable bandwidth mean shift and data-driven scale selection[C]//Proceedings of the 8th International Conference of Computer Vision.Vancouver:IEEE,2001:438-445.
[15] Baatz M,Schäpe A.Multiresolution segmentation:An optimization approach for high quality multi-scale image segmentation[M]//Strobl J,Baschke T,Griesebner G.Angewandte Geographische Informations Verarbeitung XII.Karlsruhe:Wichmann Verlag,2000:12-23.
[16] Fukunaga K,Hostetler L.The estimation of the gradient of a density function,with applications in pattern recognition[J].IEEE Transactions on Information Theory,1975,21(1):32-40.
[17] Qin A K,Clausi D A.Multivariate image segmentation using semantic region growing with adaptive edge penalty[J].IEEE Transactions on Image Processing,2010,19(8):2157-2170.
[18] 苏腾飞,李洪玉.一种两阶段区域生长的遥感图像分割算法[J].遥感技术与应用,2015,30(3):476-485.
Su T F,Li H Y.A two stage region growing method for remote sensing image segmentation[J].Remote Sensing Technology and Application,2015,30(3):476-485.
[19] Jiang H B,Su Y Y,Jiao Q S,et al.Typical geologic disaster surveying in Wenchuan 8.0 earthquake zone using high resolution ground LiDAR and UAV remote sensing[C]//Proceedings of SPIE 9262,Lidar Remote Sensing for Environmental Monitoring XIV.Beijing,China:SPIE,2014:926219.
[20] Yi L N,Zhang G F,Wu Z C.A scale-synthesis method for high spatial resolution remote sensing image segmentation[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(10):4062-4070.
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